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中文摘要：
为了避免传统小波变换在进行信号特征提取时,过分依赖于小波基的选择,引入了冗余第二代小波变换。对不同层的原始预测器和更新器进行插值补零运算,得出对应层的冗余预测器和更新器。然后利用新的冗余预测器和更新器对原始信号进行分解,使得分解的高频信号和低频信号的长度与原始信号长度相等。对齿轮箱故障特征提取表明,冗余第二代小波变换优于其他小波变换方法,能够比较理想地提取出齿轮箱的故障特征。
英文摘要：
In order to avoid the excessive reliance on the selection of wavelet basis when traditional wavelet transform extracting signal features, a novel method based on redundant the second generation wavelet is introduced. The initial predictor and updater are interpolated with zero at different decomposition scale to gain the redundant predictor and updater of the corresponding layer. Then the original signal is decomposed through the new redundant predictor and updater, making the length of the decomposition of the high-frequency signal and low-frequency signal equal to that of the original signal. The results of the gearbox feature extraction show that the application of the redundant the second generation wavelet is better than the traditional wavelet and is more effective in extracting the fault feature information of gearbox.
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